Various types of field measurements provide important data regarding the conditions in a field, including the environmental and/or seed-bed conditions in a field. For example, field measurements can include crop residue measurements (e.g., percent crop residue cover), soil roughness measurements (e.g., measurements of average soil clod size or the like), and/or other measurements of a field and its characteristics. These measurements are of high value to agricultural operators to understand current conditions of the field and, if needed, to modify the conditions of the field to be more optimal.
As one example, for various reasons, it is important to maintain a given amount of crop residue within a field following an agricultural operation. Specifically, crop residue remaining within the field can help in maintaining the content of organic matter within the soil and can also serve to protect the soil from wind and water erosion. However, in some cases, leaving an excessive amount of crop residue within a field can have a negative effect on the soil's productivity potential, such as by slowing down the warming of the soil at planting time and/or by slowing down seed germination. As such, the ability to monitor and/or adjust the amount of crop residue remaining within a field can be very important to maintaining a healthy, productive field, particularly when it comes to performing tillage operations.
As another example, for various reasons, it is important to maintain a given amount of soil roughness within a field before or following an agricultural operation. For example, when planting seeds it is generally not desired to have soil clods that are larger than a certain size.
In the past, these field measurements have been manually generated by a human operator/planter. More recently, automatic field measurement systems have been developed that generate these field measurements automatically or in a partially-automated fashion. Typically these automatic field measurement systems deploy or otherwise leverage a number of sensors, such as vision sensors, to produce the field measurements.
In particular, these automatic field measurement systems typically provide field measurement data expressed according to an automatic system metric associated with the automatic field measurement system. However, this automatic system metric may not accurately scale with established agronomical measurements. Thus, the output of the automatic field measurement system may be confusing or otherwise difficult to use due to its failure to scale accurately with more established agronomy metrics promulgated by various agronomy experts or organizations.